Rapid advances in the type and quality of genetic information has dramatically improved our ability to consider the effects of specific genes on complex traits related to health outcomes in aging. Genomic information about genetic polymorphisms and our ability to quantify massive amounts of information regarding gene expression requires new methodologies but provides new opportunities to understand the complex factors that contribute to familial resemblance. Less attention has been paid to incorporating environmental factors into models of familial influences even though a substantial amount of individual variability is viewed as the result of environmental variation. The development of comprehensive models that effectively incorporate extensive measurable information on both genetic and environmental risk factors has the potential to-give us the tools to more effectively understand the processes that influence health outcomes in aging and to develop interventions that address complexities such as gene-by-environment interaction that, until now, have been largely beyond our reach. One of the few environmental factors that has been clearly connected to age-related morbidities and mortality is socioeconomic status (SES), and researchers have offered some compelling theories as to how these relationships arise. Better assessments of SES that are informed by lifespan and contextual perspectives are needed to be able to incorporate this multi-dimensional environmental factor into models that include genetic influences. The purpose of this project is to convene a network of experts including geneticists, behavioral scientists representing psychology and sociology, social methodologists and statisticians to consider measurement issues and to develop analytic methods that allow us to incorporate aspects of the measured environment into models that also include genetic information. The network will employ a mix of workshop-type meetings, regular informal meetings, and web-based communication tools. The goal of the network is to identify/formulate intellectual and analytical tools to understand the complex effects and interactions that contribute to morbidity and mortality in aging. These tools will be useful in understanding individual variability and in determining the origin of health disparities among groups.